CT acquisition using PET detectors and electronics
Bibliographic record
Abstract
The emergence of PET/CT multi-modality imaging has provided the ability to sequentially obtain anatomic and functional information using adjacent PET and CT scanners without having to move the patient. To avoid the need for successive PET and CT scans, we have investigated the possibility of acquiring both the anatomic and functional images using the same detection system, based on conventional PET detectors and electronics operated in photon counting mode. The detector consisted of a high-luminosity LSO scintillator individually coupled to an avalanche photodiode to enable low-energy X-ray detection at high-count rate. A simulator was set up to collect tomographic data using a monochromatic 60 keV source (/sup 241/Am) to irradiate a phantom made of tissue-equivalent materials. The measured spatial resolution with this non-optimized setup was nearly 1 mm, demonstrating the capability to provide fairly accurate anatomical localization in CT counting mode. The images obtained showed that the three main constituents of a biological body (bones, water and air) can be clearly identified with a dose significantly lower than with conventional CT operated in current mode. These preliminary results demonstrate the feasibility of dual-modality PET/CT imaging based on PET detectors and electronics, and confirm that substantial dose reduction would be possible by acquiring the CT image in photon counting mode.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".